A Prediksi Prestasi Siswa Menggunakan Algoritma Decision Tree C4.5 - Umi Halimatussa'diyah; Abdul Rohman (Jurnal Algoritma, 2026)
This study aims to predict student achievement levels at MA Darul Muna using the C4.5 Decision Tree algorithm as a data mining method to support academic decision making. The data used consists of scores for several core subjects, including Al-Qur'an Hadith, Aqidah, Fiqh, PPKN, and Indonesian Language. The research stages included data collection and pre-processing, C4.5 model formation, and evaluation using accuracy, precision, and recall. The resulting model achieved 89.7% accuracy for the student dataset for one academic year, indicating that subject grades were the most influential attribute in achievement classification. These findings indicate that C4.5 can help schools identify students who are potentially underperforming so that learning interventions can be implemented more quickly. The research is still limited in terms of the number of academic variables and the scope of a single institution, so further research opportunities exist in developing non-academic variables and a broader dataset.